class_docstr: An implementation of Squeeze-and-Excitation Block.
  
  Arguments
  ---------
  channels : int
      The number of channels.
  reduction : int
      The reduction factor of channels.
  
  Example
  -------
  >>> inp_tensor = torch.rand([1, 64, 80, 40])
  >>> se_layer = SEBlock(64)
  >>> out_tensor = se_layer(inp_tensor)
  >>> out_tensor.shape
  torch.Size([1, 64, 80, 40])
docstr: Intermediate step. Processes the input tensor x
  and returns an output tensor.
docstr_tok: ["Intermediate","step",".","Processes","the","input","tensor","x","and","returns","an","output","tensor","."]
code: def forward(self, x):
      """Intermediate step. Processes the input tensor x
      and returns an output tensor.
      """
      b, c, _, _ = x.size()
      y = self.avg_pool(x).view(b, c)
      y = self.fc(y).view(b, c, 1, 1)
      return x * y
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  from speechbrain.nnet.normalization import BatchNorm1d
  from speechbrain.nnet.linear import Linear
label_desc: Use image node_id 2 for calling the SEBlock obj's underlying member method code with example usage: obj.forward(x) and returns: unknown